Autotuning method using integral of relay feedback response for extracting process information

ABSTRACT

An autotuning method using an integral of a relay feedback response is disclosed, which obtains an ultimate data and frequency model of a process with greatly removing effects of harmonics by using integral of a relay feedback signal. The autotuning method using an integral of a relay feedback response comprises the first step for integrating a process data; and the second step for removing effects of harmonics after the first step and computing an ultimate gain, a frequency model and a parametric process model.

CLAIM OF PRIORITY

This application claims the benefit of Korean Patent Application No. 2006-112623 filed on 15, Nov., 2006, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Cross Reference to the Related Art

[1] Abramowitz, M, Stegun, I A. Handbook of mathematical functions, Dover Publications, NY, 1972.

[2] Astrom, K J, Hagglund, T. Automatic tuning of simple regulators with specifications on phase and amplitude margins. Automatica, 20, 645, 1984.

[3] Astrom, K J, Hagglund, T. PID controllers. 2^(nd) ed., Instrument Society of America, NC, 1995

[4] Chiang, R, Yu, C. Monitoring Procedure for Intelligent Control: On-Line Identification of Maximum Closed-Loop Log Modulus, Ind. Eng. Chem. Research, 32, 90, 1993

[5] Friman M, Waller, K V. A two channel relay for autotuning. Ind. Eng. Chem. Research, 36, 2662-2671, 1997.

[6] Hang, C C, Astrom, K J, Wang Q G. Relay feedback auto-tuning of process controllers—a tutorial review. J. Process Control, 12, 143-162, 2002.

[7] Hang, C C, Astrom, K J, Ho, W K. Relay auto-tuning in the presence of static load disturbance. Automatica, 29, 563-564, 1993.

[8] Huang, H P, Jeng, J C, Luo, K Y. Auto-tune system using single-run relay feedback test and model-based controller design. J. Process Control, 15, 713-727, 2005.

[9] Kaya, I, Atherton, D P. Parameter estimation from relay autotuning with asymmetric limit cycle data. J. Process Control, 11, 429-439, 2001.

[10] Kim, Y H. P I Controller Tuning Using Modified Relay Feedback Method, J. Chem. Eng. Japan, 28, 118, 1995.

[11] Kreyszig, E. Advanced engineering mathematics. 8^(th) ed., New York, Wiley, 1999.

[12] Luyben, W L. Getting more information from relay feedback tests. Ind. Eng. Chem. Research, 40, 4391, 2001.

[13] Ma, M D, Zhu, X J. A simple auto-tuner in frequency domain. Comp. Chem. Eng., 2006.

[14] Panda, R C, Yu, C C. Analytic expressions for relay feedback responses. J. Process Control, 13, 48, 2003.

[15] Panda, R C, Yu, C C. Shape factor of relay response curves and its use in autotuning. J. Process Control, 15, 893-906, 2005.

[16] Shen, S H, Wu, J S, Yu, C C. Use of biased relay feedback method for system identification. AIChE J., 42, 1174-1180, 1996a.

[17] Shen, S H, Wu, J S, Yu, C C. Autotune identification under load disturbance. Ind. Eng. Chem. Research, 35, 1642-1651, 1996b.

[18] Sung, S W, Park, J H, Lee, I. Modified relay feedback method. Ind. Eng. Chem. Research, 34, 4133-4135, 1995.

[19] Sung, S W, Lee, I. Enhanced relay feedback method, Ind. Eng. Chem. Research, 36, 5526, 1997.

[20] Sung, S W, Lee, J., Lee, D H, Han, J H, Park, Y S. A Two-Channel Relay Feedback Method under Static Disturbances, Ind. Eng. Chem. Research, 45, 4071, 2006.

[21] Sung, S W, Lee, J. Relay feedback method under large static disturbances. Automatica, 42, 353-356, 2006.

[22] Sung, S W, O, J, Lee, I, Lee, J, Yi, S H. Automatic Tuning of PID Controller using Second-Order Plus Time Delay Model, J. Chem. Eng. Japan, 29, 990, 1996.

[23] Tan, K K, Lee, T H, Huang, S, Chua, K Y, Ferdous, R. Improved critical point estimation using a preload relay. J. Process Control, 2005.

[24] Tan, K K, Lee, T H, Wang, Q G. Enhanced automatic tuning procedure for process control of PI/PID controllers, AIChE J., 42, 2555, 1996.

[25] Wang, Q G, Hang, C C, Bi, Q. Process frequency response estimation from relay feedback. Control Eng. Practice, 5, 1293-1302, 1997a.

[26] Wang, Q G, Hang, C C, Zou, B. Low order modeling from relay feedback. Ind. Eng. Chem. Research, 36, 375, 1997b.

[27] Yu, C C. Autotuning of PID controllers: A relay feedback approach. 2^(nd) ed. Springer, London, 2006.

2. Field of the Invention

The present invention relates to an autotuning method for a PID (Proportional Integral Derivative) controller, and in particular to an autotuning method using an integral of a relay feedback response in which an ultimate data and a frequency model of a process can be reliably obtained by integrating a relay feedback signal and removing a lot of harmonics.

3. Description of the Related Art

PID controller has been widely used in various industrial facilities due to its simple structure and excellent control performance. The PID controller may solely use a proportional control, an integral control or a derivative control or may combine and use at least two controls of the above methods.

The relay feedback methods for autotuning of a PID controller have been widely used. In the conventional relay feedback methods, a frequency model of a process is computed using an oscillation period and an oscillation magnitude of input and output responses. The above method will be described in details as follows.

Since [2] Astrom and Hagglund (1984) introduced the autotuning method, which used the relay feedback test, many variations have been proposed for autotuning of PID controllers ([3] Astrom and Hagglund, 1995; Hang et al., 2002; Yu, 2006). Several methods such as a saturation relay ([27] Yu, 2006), relay with a P control preload ([23] Tan et al., 2005) and a two level relay ([28] Sung et al., 1995) were introduced to obtain more accurate ultimate data of the process by suppressing the effects of the high order harmonic terms. To obtain a Nyquist point other than the ultimate point, a relay with hysteresis or a dynamic element such as time delay has been used ([3] Astrom and Hagglund, 1995; [10] Kim, 1995; [24] Tan et al., 1996; [4] Chiang and Yu, 1993). Recently, a two channel relay has been proposed to obtain a Nyquist point information corresponding to a given phase angle ([5] Friman and Waller, 1997; [20] Sung et al., 2006). Methods to reject unknown load disturbances and restore symmetric relay oscillations have been available ([7] Hang et al., 1993; [17] Shen et al, 1996b; [21] Sung and Lee, 2006). A biased relay has been used to obtain the process steady state gain as well as the ultimate data ([16] Shen et al., 1996a) from only one relay test. [8] Huang et al. (2005) used the integral of the relay transient to obtain the steady state gain of the process.

Many Nyquist points of the process dynamics can be extracted from only one relay experiment by applying the FFT (fast Fourier transformation) technique to the whole transient responses from the start to the final cyclic steady-state part of the relay responses ([25] Wang et al., 1997a). However, the computations are somewhat complex and the complete transient responses must be stored. For the same purpose, Laplace transformation of a periodic function has been used to obtain many frequency responses from one relay test ([13] Ma and Zhu, 2006).

The shape factor (Luyben, 2001) has been used to extract a three-parameter model from the cyclic steady state part of the relay response. Several authors ([9] Kaya and Atherton, 2001;

Panda and Yu, 2003) derived exact expressions relating the parameters of the FOPTD process to the measured data of the relay response. They used the analytic expressions to extract parameters of the FOPTD model. However, the methods based only on the cyclic steady state data cannot provide acceptable robustness for uncertainty such as process/model mismatches and nonlinearity. They may provide poor model parameter estimates such as negative gain when the model structure is different from that of the process ([15] Panda and Yu, 2005). The second order plus time delay (SOPTD) model can be also extracted from the cyclic steady state part of the relay response using analytic equations. However, as in the FOPTD model case, the method is also not robust. A relay experiment with a subsequent P control experiment or another relay feedback test can be used to obtain an SOPTD model robustly ([22] Sung et al., 1996; [19] Sung and Lee, 1997).

SUMMARY OF THE INVENTION

The present invention has been made to solve the foregoing problems of the prior art and its object is to provide an autotuning method.

It is another object of the present invention to provide an autotuning method which obtains an ultimate data and frequency model of a process with greatly removing effects of harmonics by using an integral of relay feedback signal.

To achieve the above objects, there is provided an autotuning method using an integral of a relay feedback response which comprises:

the first step for integrating a process data; and

the second step for removing any effects of harmonics after the first step and computing an ultimate gain.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and other advantages of the present invention will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, in which:

FIG. 1 is a view for describing a conventional relay feedback method and integral;

FIG. 2 is a view for describing a conventional relay feedback signal response and integral response;

FIG. 3 is a flow chart of an autotuning method using an integral of a relay feedback response according to an embodiment of the present invention;

FIG. 4 is a detailed flow chart for a provision of a parameter model of FIG. 3;

FIG. 5 is a view illustrating a relay signal response and a sine signal in a cyclic steady state;

FIG. 6 is a view illustrating a ultimate data modeling performance of a proposed method and a conventional method in case of (G(s)=exp(−ds)/(τs+1)) of a first order time delay process;

FIG. 7 is a view illustrating a frequency model performance of a proposed method and a conventional method in case of (G(s)=exp(−ds)/(τs+1)) of a first order time delay process;

FIG. 8 is a view illustrating a time constant modeling performance in case of (G(s)=exp(−ds)/(τs+1)) of a first order time delay process;

FIG. 9 is a view illustrating a time constant modeling performance with respect to a measurement error in case of (G(s)=exp(−ds)/(τs+1)) of a first order time delay process;

FIG. 10 is a view illustrating a relationship between a curvature and a (G(s)=exp(−ds)/(τs+1)) of a first order time delay process;

FIG. 11 is a view illustrating a first order time delay modeling performance (Solid line: Eq. (32), dotted line: Eq. (34)+Eq. (37), dashed line: Eq. (35)+Eq. (38) and Eq. (36)+Eq. (39)) in a higher order process;

FIG. 12 is a view illustrating a first order time delay modeling performance (Solid line: FOPTD model, dotted line: CDPTD model, dashed line: SOPTD model) in a higher order process;

FIG. 13 is a view illustrating a relay feedback response under a measurement noise condition; and

FIG. 14 is a view illustrating a modeling performance (Solid line: process, dotted line: FOPTD model, dash-dotted line: CDPTD model, and dashed line: SOPTD model).

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION

The autotuning method using an integral of a relay feedback response according to an embodiment of the present invention will be described with reference to the accompanying drawings.

FIG. 2 is a view for describing a conventional relay feedback signal response and integral response, and FIG. 3 is a flow chart of an autotuning method using an integral of a relay feedback response according to an embodiment of the present invention.

As shown therein, an autotuning method using an integral of a relay feedback response comprises: the first step(ST1) for integrating a process data; and the second step(ST2) for removing any effects of harmonics after the first step and computing an ultimate gain.

The autotuning method using an integral of a relay feedback response further comprises the third step(ST3) for providing a frequency process model and a parameter model after the second step.

As shown in FIG. 3, the third step comprises a step 3-1 (ST3-1) for setting a second time delay model of

${{G_{m}(S)} = \frac{k\; {\exp \left( {- {ds}} \right)}}{{a_{2}s^{2}} + {a_{1}s} + 1}};$

a step 3-2(ST3-2) for obtaining G_(m)(0), G′_(m)(0), q_(b) and the oscillation period P from relay feedback response and determining d=p/4; a step 3-3(ST3-3) for computing a₁ from k(−d−a₁)=G¹ _(m)(0) after the step 3-2; a step 3-4(ST3-4) for computing a₂ from

$q_{b} = {{\frac{16\; h^{2}}{\pi^{2}\omega^{2}}\left( {{G_{m}({j\omega})}}^{2} \right)} = {\frac{16\; h^{2}}{\pi^{2}\omega^{2}}\frac{k^{2}}{\left( {1 - {a_{2}\omega^{2}}} \right)^{2} + \left( {a_{1}\omega} \right)^{2}}}}$

after the step 3-3; and a step 3-5(ST3-5) for adjusting d so that

$d = {\frac{p}{2}\left( {1 - {\arctan \; \left( {q_{c}/\omega} \right)} - {\arctan \; 2\left( {{1 - {a_{1}\omega^{2}}},{a_{2}\omega}} \right)}} \right)}$

is satisfied after the step 3-4.

The autotuning method using an integral of a relay feedback response further comprises the fourth step for computing and applying a controller tuning parameter using a tuning method after the third step.

The autotuning method using an integral of a relay feedback response according to an embodiment of the present invention will be described in details with reference to the accompanying drawings. In the description of the present invention, when the detailed description with respect to the known function or construction could make the subject matters of the present invention unobvious, the detailed description of the same will be omitted. The terms, which will be described later, are defined in consideration with the functions of the present invention. The terms may change based on the intention or judicial precedent, and the meaning of each term should be interpreted based on the contents of the specification of the present invention.

In the present invention, an ultimate data and frequency model of a process are supposed to be obtained by integrating and using a relay feedback signal and greatly removing effects of harmonics. In the conventional art, a frequency model of a process is computed using an oscillation period and an oscillation magnitude of a process input and output data. In the present invention, a new method is disclosed, in which a signal obtained by integrating a process data is used as compared to a conventional art in which an input and output data of a process are directly used. In the present invention, an accuracy of an obtained frequency process model and parameter model are greatly enhanced since the effects of harmonics are largely removed using an integral of a process data.

So, the present invention discloses a method for obtaining an accurate frequency information and parameter model from a conventional relay feedback response. According to the method of the present invention, a more accurate frequency process model and parameter model can be obtained by using a signal obtained by integrating a process data and removing effects of a harmonic term in order to enhance a process modeling performance as compared to the conventional art in which an input and output data of a process are directly used. In the present invention, it does not need to store all the relay feedback response data, and the computational load is very light. The present invention may be well adapted to an industrial PID controller which uses a low performance microprocessor.

1. Conventional Relay Feedback Method

We consider the classical relay feedback system as shown in FIG. 1 and FIG. 2 to derive the required equations in this research. We start the relay feedback system at a steady state condition.

As shown in FIG. 1, U_(a)(s) is a process input, and Y_(a)(s) is a process output, and 1/s is an integral, and U_(b)(s) is an integral signal of a process input, and Y_(b)(s) is an integral signal of a process output, and G(s) is a transfer function of a process.

In the first step, a relay on output is applied to a process until a process output increases and reaches a certain given value. In the second step, when a process output is greater than the initial state, a relay off output is adapted. When the process output is opposite, a relay on output is adapted. After three cycles in the above manner, the process output and the relay output become a cyclic steady state. Namely, a process output has a certain oscillation period and magnitude.

[2] Astrom and Hagglund (1984) used this oscillation to extract approximate ultimate data and tune the proportional-integral-derivative (PID) controllers automatically. Let the input and output trajectories be u_(a)(t) and y_(a)(t) for the conventional relay feedback system, respectively, when t_(1 , u) _(a)(t) and y_(a)(t) are assumed to be fully developed (cyclic steady state). Then it can be represented by the Fourier series as

$\begin{matrix} {{u_{a}\left( \overset{\sim}{t} \right)} = {\frac{4h}{\pi}\left( {{\sin \; \left( {\omega \; \overset{\sim}{t}} \right)} + {\frac{1}{3}{\sin \left( {3\omega \; \overset{\sim}{t}} \right)}} + {\frac{1}{5}\sin \; \left( {5\; \omega \; \overset{\sim}{t}} \right)} + \ldots}\mspace{11mu} \right)}} & \left\lbrack {{Equation}\mspace{20mu} 1} \right\rbrack \end{matrix}$

where {tilde over (t)}=t−t₁ and h is the relay amplitude. Let p and ω=2π/p are the period and the frequency of the relay feedback oscillation. Equation 1 is valid for

{tilde over (t)}=t−t₁≧   [Equation 2]

The output corresponding to u_(a)({tilde over (t)}) is

$\begin{matrix} {{y_{a}\left( \overset{\sim}{t} \right)} = {\frac{4h}{\pi}\begin{pmatrix} {{{{G({j\omega})}}\sin \; \left( {{\omega \; \overset{\sim}{t}} + {\angle \; {G({j\omega})}}} \right)} +} \\ {{\frac{1}{3}{{G({j3\omega})}}\sin \; \left( {{3\omega \; \overset{\sim}{t}} + {\angle \; {G({j3\omega})}}} \right)} + \ldots} \end{pmatrix}}} & \left\lbrack {{Equation}\mspace{20mu} 3} \right\rbrack \end{matrix}$

where G(s) is the process transfer function. Neglecting the high harmonic terms and assuming ∠G(jω)≈−π, we obtain u_(a)({tilde over (t)})≈4 h sin(ω_(n){tilde over (t)})/π, y_(a)({tilde over (t)})≈4 h|G(jω_(u))| sin(ω_(u){tilde over (t)}+∠G(jω_(u)))/π≈−4 h|G(jω_(u)) sin(ω_(u){tilde over (t)})/π. We obtain the following approximate ultimate period P_(u) and ultimate gain K_(cu) as

$\begin{matrix} {P_{u} = p} & \left\lbrack {{Equation}\mspace{20mu} 4} \right\rbrack \\ {K_{cu} = \frac{4h}{\pi \; a}} & \left\lbrack {{Equation}\mspace{20mu} 5} \right\rbrack \end{matrix}$

where P_(u) and K_(cu) are the ultimate period and the ultimate gain, respectively, and a is the measured amplitude of y_(a)(t). It should be noted that the estimated ultimate data are approximate because we neglect the high harmonic terms. As a result, the ultimate period of equation 4 and the ultimate gain of equation 5 show relative errors up to 5% and 18%, respectively, for the first order plus time delay (FOPTD) process. In this case, the ultimate gain error may not be acceptable. It is noted that the ultimate gain error of 18% decreases the controller performance significantly.

2. Proposed Methods for Ultimate Data Estimation

In this research, we use integrals of the process input and output instead of the point data to obtain more accurate process frequency information and parametric process models by suppressing the effects of high harmonic terms.

Let u_(b)(t) and y_(b)(t) be the integrals of the relay responses as

u _(b)(t)=∫₀ u _(a)(t)dt   [Equation 6]

y _(b)(t)=∫₀ y _(a)(t)dt   [Equation 7]

Then, u_(b)(t) is an integral signal of the process input, and y_(b)(t) is an integral signal of the process output and from equation 1, the response u_(b)(t) after t₁ is

$\begin{matrix} {{u_{b}\left( \overset{\sim}{t} \right)} = {u_{bm} - {\frac{4h}{\pi \; \omega}\left( \begin{matrix} {{\cos \; \left( {\omega \; \overset{\sim}{t}} \right)} + {\frac{1}{9}\; \cos \; \left( {3\omega \; \overset{\sim}{t}} \right)} +} \\ {{\frac{1}{25}{\cos \left( {5\; \omega \; \overset{\sim}{t}} \right)}} + \ldots} \end{matrix}\mspace{11mu} \right)}}} & \left\lbrack {{Equation}\mspace{20mu} 8} \right\rbrack \end{matrix}$

where u_(bm) is the mean value of u_(b)(t)

$\begin{matrix} {u_{bm} = {\frac{1}{p}{\int_{t_{1}}^{t_{1} + p}{{u_{b}(t)}{t}}}}} & \left\lbrack {{Equation}\mspace{20mu} 9} \right\rbrack \end{matrix}$

From equation 3, the response y_(b)(t) after t₁ can be represented as

$\begin{matrix} {{y_{b}\left( \overset{\sim}{t} \right)} = {y_{bm} - {\frac{4h}{\pi\omega}\begin{pmatrix} {{{{G({j\omega})}}\cos \; \left( {{\omega \; \overset{\sim}{t}} + {\angle \; {G({j\omega})}}} \right)} +} \\ {{\frac{1}{9}{{G({j3\omega})}}\cos \; \left( {{3\omega \; \overset{\sim}{t}} + {\angle \; {G({j3\omega})}}} \right)} + \ldots} \end{pmatrix}}}} & \left\lbrack {{Equation}\mspace{20mu} 10} \right\rbrack \end{matrix}$

where y_(bm) is the mean value of y_(b)(t).

$\begin{matrix} {y_{bm} = {\frac{1}{p}{\int_{t_{1}}^{t_{1} + p}{{y_{b}(t)}{{t}/p}}}}} & \left\lbrack {{Equation}\mspace{20mu} 11} \right\rbrack \end{matrix}$

Then, we can construct the following response signal.

$\begin{matrix} \begin{matrix} {{u_{c}\left( \overset{\sim}{t} \right)} = {{u_{a}\left( \overset{\sim}{t} \right)} + {\frac{6\pi}{p}\left( {{u_{b}\left( {\overset{\sim}{t} + {p/4}} \right)} - u_{bm}} \right)}}} \\ {= {{\frac{16h}{\pi}\sin \; \left( {\omega \; \overset{\sim}{t}} \right)} + {\frac{52h}{25\pi}{\sin \left( {5\; \omega \; \overset{\sim}{t}} \right)}} + \ldots}} \end{matrix} & \left\lbrack {{Equation}\mspace{20mu} 12} \right\rbrack \\ {{y_{c}\left( \overset{\sim}{t} \right)} = {{y_{a}\left( \overset{\sim}{t} \right)} + {\frac{6\pi}{p}\left( {{y_{b}\left( {\overset{\sim}{t} + {p/4}} \right)} - y_{bm}} \right)}}} & \left\lbrack {{Equation}\mspace{20mu} 13} \right\rbrack \end{matrix}$

We should remark that u_(a)({tilde over (t)}) is a rectangular wave, u_(b)({tilde over (t)}) is a triangular wave and u_(c)({tilde over (t)}) is their combination such that the third harmonic term vanishes. The forcing functions of u_(b)({tilde over (t)}) and u_(c)({tilde over (t)}) are closer to a sinusoidal wave than u_(a)({tilde over (t)}). Hence, y_(b)({tilde over (t)}) and y_(c)({tilde over (t)}) are closer to the sinusoidal wave than y_(c)({tilde over (t)}). FIG. 5 shows typical plots of these responses.

By neglecting the high harmonic terms and assuming ∠G(jω)=−π (equivalently, the relay period is the ultimate period), we obtain u_(b)({tilde over (t)})≈−4 h cos(ω_(u){tilde over (t)})/π, y_(b)({tilde over (t)})≈4 h|G(jω)| cos(ω_(u){tilde over (t)})/π. Then, we have the following approximate ultimate gain K_(cu).

$\begin{matrix} {K_{cu} = \frac{2h\; p}{\pi^{2}b}} & \left\lbrack {{Equation}\mspace{20mu} 14} \right\rbrack \end{matrix}$

Here, b is the amplitude of y_(b)({tilde over (t)})−y_(bm). The quantity b is physically the half of the shaded area of y_(a)({tilde over (t)}) in FIG. 2. As seen in the equations 1, 3, 8 and 10, in y_(b)({tilde over (t)}), the fundamental term is much higher than harmonic term as compared to y_(a)({tilde over (t)}). So, the equation 14 proposed in the present invention has much more accurate ultimate data as compared to the conventional equation 5.

The following equation is derived from the equations 12 and 13.

$\begin{matrix} {K_{cu} = {\frac{16h}{\pi \left( {a + {6\; \pi \; {b/p}}} \right)} = \frac{1}{{\frac{1}{4}\left( \frac{\pi \; a}{4h} \right)} + {\frac{3}{4}\left( \frac{\pi^{2}b}{2h\; p} \right)}}}} & \left\lbrack {{Equation}\mspace{20mu} 15} \right\rbrack \\ \begin{matrix} {q_{a} \equiv {\frac{2}{p}{\int_{t_{1}}^{t_{1 + p}}{{y_{a}(t)}^{2}{t}}}}} \\ {= {\frac{16\; h^{2}}{\pi^{2}}\left( {{{G({j\omega})}}^{2} + {\frac{1}{9}{{G\left( {j\; 3\; \omega} \right)}}^{2}} + \ldots} \right)}} \end{matrix} & \left\lbrack {{Equation}\mspace{20mu} 16} \right\rbrack \\ \begin{matrix} {q_{b} \equiv {{\frac{2}{p}{\int_{t_{1}}^{t_{1 + p}}{{y_{b}(t)}^{2}{t}}}} - {2y_{bm}^{2}}}} \\ {= {\frac{16\; h^{2}}{\pi^{2}\omega^{2}}\left( {{{G({j\omega})}}^{2} + {\frac{1}{81}{{G\left( {j\; 3\; \omega} \right)}}^{2}} + \ldots} \right)}} \end{matrix} & \left\lbrack {{Equation}\mspace{20mu} 17} \right\rbrack \end{matrix}$

The equation 16 and 17 are derived by applying orthogonality of sine and cosine functions of the equations 3 and 10(refer to appendix A). To compute the equations 16 and 17, since it is not needed to store all data of y_(a)(t) and y_(b)(t), the computation may be available using a simple microprocessor. By ignoring the high harmonic terms in q_(a), q_(b), we have

$\begin{matrix} {K_{cu} = \frac{4h}{\pi \sqrt{q_{a}}}} & \left\lbrack {{Equation}\mspace{20mu} 18} \right\rbrack \\ {K_{cu} = \frac{2h\; p}{\pi^{2}\sqrt{q_{b}}}} & \left\lbrack {{Equation}\mspace{20mu} 19} \right\rbrack \end{matrix}$

When y_(a)({tilde over (t)}) and y_(b)({tilde over (t)}) are sinusoidal, a=√{square root over (q_(b))} and b=√{square root over (q_(b))} provide the same results.

FIG. 6 shows relative errors in estimated ultimate period and ultimate gains. For FOPTD processes with ratios of time delays to time constants between 0.1 and 5, equations 14, 15, 18 and 19 have relative errors below about 6%. We can see that the errors in equation 5 for the ultimate gain can be improved considerably.

3. Proposed Frequency Model Computation Method

In the previous section, we estimate the ultimate gain on the assumption that the period of relay feedback oscillation is the ultimate period. We omit that assumption in this section

Our goal in this section is to find the following amplitude ratio and the phase lag of the process at the frequency of relay oscillation.

$\begin{matrix} {{{G({j\omega})} = {A_{\omega}{\exp \left( {j\left( {{- \pi} + \varphi_{\omega}} \right)} \right)}}},{\omega = \frac{2\pi}{p}}} & \left\lbrack {{Equation}\mspace{20mu} 20} \right\rbrack \end{matrix}$

where, A_(ω)=|G(jω)| and φ_(ω)=π+∠G(jω) show the amplitude ratio and phase angle of each process. From equation 16, we can obtain the approximate amplitude ratio as follow, at the frequency ω=2π/p by neglecting high harmonic terms.

$\begin{matrix} {{\overset{\Cap}{A}}_{\omega} = \frac{\pi \sqrt{q_{a}}}{4h}} & \left\lbrack {{Equation}\mspace{20mu} 21} \right\rbrack \end{matrix}$

When |G(jnω)|<|G(jω)|, n=2,3, . . . , its approximation error is |(Â_(ω)−|G(jω)|)/|G(jω)||≦√{square root over (1+⅓²+⅕²+ . . . )}−1=0.11

Now, consider the following quantity.

$\begin{matrix} \begin{matrix} {q_{c} = \begin{matrix} {\left\lbrack {\frac{2}{p}{\int_{t_{1}}^{t_{1} + p}{{u_{a}(t)}{y_{b}(t)}{t}}}} \right\rbrack/} \\ \left\lbrack {{\frac{2}{p}{\int_{t_{1}}^{t_{1} + p}{{u_{b}(t)}{y_{b}(t)}{t}}}} - {2u_{bm}y_{bm}}} \right\rbrack \end{matrix}} \\ {= \frac{\frac{8\; p\; h^{2}}{\pi^{3}}\begin{bmatrix} {{\sin \left( {\angle \; {G({j\omega})}} \right)} +} \\ {{\frac{{G({j3\omega})}}{27{{G({j\omega})}}}{\sin \left( {\angle \; {G({j3\omega})}} \right)}} + \ldots} \end{bmatrix}}{\frac{4\; p^{2}\; h^{2}}{\pi^{4}}\begin{bmatrix} {{\cos \left( {\angle \; {G({j\omega})}} \right)} +} \\ {{\frac{{G({j3\omega})}}{81{{G({j\omega})}}}{\cos \left( {\angle \; {G({j3\omega})}} \right)}} + \ldots} \end{bmatrix}}} \\ {= {\frac{2\pi}{p}\begin{bmatrix} {\frac{\sin \; \left( \varphi_{\omega} \right)}{\cos \left( \varphi_{\omega} \right)} +} \\ {{\frac{{G\left( {j\; 3\omega} \right)}}{{G({j\omega})}}\begin{pmatrix} {{- \frac{\sin \left( {\angle \; {G({j3\omega})}} \right)}{27\; \cos \; \left( \varphi_{\omega} \right)}} +} \\ \frac{{\sin \left( \varphi_{\omega} \right)}{\cos \left( {\angle \; {G({j3\omega})}} \right)}}{81\; {\cos^{2}\left( \varphi_{\omega} \right)}} \end{pmatrix}} + \ldots} \end{bmatrix}}} \end{matrix} & \left\lbrack {{Equation}\mspace{20mu} 22} \right\rbrack \end{matrix}$

Ignoring high harmonic terms, we have

$\begin{matrix} {\varphi_{\omega} = {\arctan \left( {\frac{p}{2\pi}q_{c}} \right)}} & \left\lbrack {{Equation}\mspace{20mu} 23} \right\rbrack \end{matrix}$

Accordingly, we can compute the amplitude ratio and phase angle of a process using the equations 21 and 23.

In addition, from the equation 17, neglecting the harmonics, the amplitude ratio estimate of the process at ω=2/p can be obtained as follows.

$\begin{matrix} {{\overset{\Cap}{A}}_{\omega} = \frac{\pi^{2}\sqrt{q_{b}}}{2h\; p}} & \left\lbrack {{Equation}\mspace{20mu} 24} \right\rbrack \end{matrix}$

When |G(jnω)|<|G(jω)|, n=2,3, . . . , its approximation error is,

$\begin{matrix} \begin{matrix} {{{\left( {A_{\omega} - {{G({j\omega})}}} \right)/{{G({j\omega})}}}} \leq {\sqrt{1 + {1/3^{4}} + {1/5^{4}} + \ldots} - 1}} \\ {= {\frac{\pi^{2}}{4\sqrt{6}} - 1}} \\ {= 0.0073} \end{matrix} & \left\lbrack {{Equation}\mspace{20mu} 25} \right\rbrack \end{matrix}$

For an actual application, the equation 25 has reliable accuracy. The phase angle is obtained in the equation 23.

FIG. 7 is a view illustrating an error of the equations 21 and 23 proposed for the first order plus time delay model.

4. Steady State Gain Computation

Process dynamic information contained in the cyclic steady state part of the relay responses corresponds to the frequency of ω=2π/p and its multiples. Therefore, to estimate the process information at the frequency zero, we need the transient relay response from the start to the cyclic steady state. Here a method to extract the process steady state information without storing the relay transient and complex computations is introduced. In [13] Ma and Zhu (2006), it is shown that

$\begin{matrix} {\begin{matrix} {{U_{a}(s)} = {{\int_{0}^{t_{1}}{{u_{a}(t)}{\exp \left( {- {st}} \right)}{t}}} + {\int_{t_{1}}^{\propto}{{u_{a}(t)}{\exp \left( {- {st}} \right)}{t}}}}} \\ {= {{\int_{0}^{t_{1}}{{u_{a}(t)}{\exp \left( {- {st}} \right)}{t}}} +}} \\ {{\frac{1}{1 - {\exp \left( {{- p}\; s} \right)}}{\int_{t_{1}}^{t_{1} + p}{{u_{a}(t)}{\exp \left( {- {st}} \right)}{t}}}}} \end{matrix}\begin{matrix} {{Y_{a}(s)} = {{\int_{0}^{t_{1}}{{y_{a}(t)}{\exp \left( {- {st}} \right)}{t}}} + {\int_{t_{1}}^{\propto}{{y_{a}(t)}{\exp \left( {- {st}} \right)}{t}}}}} \\ {= {{\int_{0}^{t_{1}}{{y_{a}(t)}{\exp \left( {- {st}} \right)}{t}}} +}} \\ {{\frac{1}{1 - {\exp \left( {{- p}\; s} \right)}}{\int_{t_{1}}^{t_{1} + p}{{y_{a}(t)}{\exp \left( {- {st}} \right)}{t}}}}} \end{matrix}} & \left\lbrack {{Equation}\mspace{20mu} 26} \right\rbrack \end{matrix}$

Let's assume,

Ũ _(a)(s)=(1−exp(−ps))U _(a)(s)

{tilde over (Y)} _(a)(s)=(1−exp(−ps))Y _(a)(s)   [Equation 27 ]

Since the transfer function of the process is G(s)={tilde over (Y)}_(a)(s)/Ũ_(a)(s), we have (Appendix B)

$\begin{matrix} {{{10\; G\; (0)} = {{\lim_{\; {s\rightarrow 0}}\frac{{\overset{\sim}{Y}}_{a}(s)}{{\overset{\sim}{U}}_{a}(s)}} = {{{{\overset{\sim}{Y}}_{a}^{\prime}(0)}/{{\overset{\sim}{U}}_{a}^{\prime}(0)}} = {y_{bm}/u_{bm}}}}}{where}{{{\overset{\sim}{U}}_{a}^{\prime}\; (0)} = {{\int_{t_{1}}^{t_{1} + p}{{u_{b}(t)}{t}}} = {p \times u_{bm}}}}{{{\overset{\sim}{Y}}_{a}^{\prime}\; (0)} = {{\int_{t_{1}}^{t_{1} + p}{{y_{b}(t)}{t}}} = {p \times y_{bm}}}}} & \left\lbrack {{Equation}\mspace{20mu} 28} \right\rbrack \end{matrix}$

The above equation 28 for the process steady state gain is equivalent to that in Huang et al. (2005).

We can obtain G′(0) (Appendix B) using the equation 29.

$\begin{matrix} {\begin{matrix} {{G^{\prime}(0)} = {\lim_{\; {s\rightarrow 0}}\frac{{{\overset{\sim}{Y}}_{a}^{\prime}(s)} - {{G(s)}{{\overset{\sim}{U}}_{a}^{\prime}(s)}}}{{\overset{\sim}{U}}_{a}(s)}}} \\ {= {\lim_{\; {s\rightarrow 0}}\frac{{{\overset{\sim}{Y}}_{a}^{''}(s)} - {{G^{\prime}(s)}{{\overset{\sim}{U}}_{a}^{\prime}(s)}} - {{G(s)}{{\overset{\sim}{U}}_{a}^{''}(s)}}}{{\overset{\sim}{U}}_{a}(s)}}} \\ {= {{- {G^{\prime}(0)}} + \frac{{{\overset{\sim}{Y}}_{a}^{''}(0)} - {{G(0)}{{\overset{\sim}{U}}_{a}^{''}(0)}}}{{\overset{\sim}{U}}_{a}^{\prime}(0)}}} \\ {= \frac{{{\overset{\sim}{Y}}_{a}^{''}(0)} - {{G(0)}{{\overset{\sim}{U}}_{a}^{''}(0)}}}{2{{\overset{\sim}{U}}_{a}^{\prime}(0)}}} \end{matrix}{where}{{{\overset{\sim}{U}}_{a}^{''}(0)} = {{2p{\int_{0}^{t_{1}}{{u_{b}(t)}{t}}}} - {2{\int_{t_{1}}^{t_{1} + p}{{{tu}_{b}(t)}{t}}}}}}{{{\overset{\sim}{Y}}_{a}^{''}(0)} = {{2p{\int_{0}^{t_{1}}{{y_{b}(t)}{t}}}} - {2{\int_{t_{1}}^{t_{1} + p}{{{ty}_{b}(t)}{t}}}}}}} & \left\lbrack {{Equation}\mspace{20mu} 29} \right\rbrack \end{matrix}$

This process information is very useful in obtaining higher order process models.

5. Proposed Methods for First Order Plus Time Delay Model Estimation

From the relay feedback responses, we can obtain the following first order plus time delay (FOPTD) model.

$\begin{matrix} {{G_{m}(s)} = \frac{k\; \exp \; \left( {- {ds}} \right)}{{\tau \; s} + 1}} & \left\lbrack {{Equation}\mspace{20mu} 30} \right\rbrack \end{matrix}$

The FOPTD model can be determined analytically by the following previous approach ([26] Wang et al., 1997; [9] Kaya and Atherton, 2001; [15] Panda and Yu, 2005).

$\begin{matrix} {k = \frac{y_{bm}}{u_{bm}}} & \left\lbrack {{Equation}\mspace{20mu} 31} \right\rbrack \\ {\tau = {\frac{p}{2}\frac{1}{\ln \left( \frac{{kh} + a}{{kh} - a} \right)}}} & \left\lbrack {{Equation}\mspace{20mu} 32} \right\rbrack \\ {d = {\tau \; \ln \; \left( \frac{{\exp \; \left( {p/\left( {2\tau} \right)} \right)} + 1}{2} \right)}} & \left\lbrack {{Equation}\mspace{20mu} 33} \right\rbrack \end{matrix}$

where a is the amplitude of process output, and p is a relay oscillation period. The question 31 corresponds to the equation 28. When the process is a first order plus time delay, the equations 31, 32 and 33 guarantee exact results.

When the process has a large time delay, the equation 33 does not guarantee an acceptable accuracy. We overcome this problem by using the integrals of the relay feedback responses. From the oscillation amplitude b of y_(b)({tilde over (t)}) instead of the peak value a of y_(a)({tilde over (t)}), we can obtain estimate of the time constant as

$\begin{matrix} {{\tau = {\left( {\frac{b}{kh} + \frac{p}{4}} \right)/{\ln \left( \chi_{1} \right)}}}{{\chi_{1}^{c_{1}} - {2\chi_{1}} + 1} = 0}{c_{1} = {p/\left( {\frac{2b}{kh} + \frac{p}{2}} \right)}}} & \left\lbrack {{Equation}\mspace{20mu} 34} \right\rbrack \end{matrix}$

This equation can be obtained by rearranging the analytic equations for p and b in Table 1 for the first order plus time delay process.

TABLE 1 Process Analytic equations $\frac{k\mspace{14mu} {\exp \left( {- {ds}} \right)}}{{\tau s} + 1}$ ${{y_{a}\left( \hat{t} \right)} = {{kh}\left( {1 - {\frac{2}{1 + \beta}{\exp \left( {{- \hat{t}}/\tau} \right)}}} \right)}},{\hat{t} = {\overset{\sim}{t} - d}},{\beta = {\exp \left( {{- p}\text{/}\left( {2\tau} \right)} \right)}},{h = {{relay}\mspace{14mu} {amplitude}}}$ p = 2τ ln(2 exp(d/τ) − 1) $a = {{kh}\frac{1 - \beta}{1 + \beta}}$ $q_{a} = {({kh})^{2}\left( {2 - \frac{8{\tau \left( {1 - \beta} \right)}}{p\left( {1 + \beta} \right)}} \right)}$ ${{y_{b}\left( \hat{t} \right)} - y_{bm}} = {{kh}\left( {\hat{t} - \tau - \frac{p}{4} + {\frac{2\tau}{1 + \beta}{\exp \left( {{- \hat{t}}/\tau} \right)}}} \right)}$ $b = {{kh}\left( {{\tau \mspace{11mu} {\ln \left( \frac{1 + \beta}{2} \right)}} + \frac{p}{4}} \right)}$ $q_{b} = {({kh})^{2}\left( {\frac{p^{2}}{24} - {2\tau^{2}} + \frac{8{\tau^{3}\left( {1 - \beta} \right)}}{p\left( {1 + \beta} \right)}} \right)}$ $F = {\frac{q_{a}}{a^{2}} = {{2\left( \frac{1 + \beta}{1 - \beta} \right)^{2}} - {\frac{8\tau}{p}\frac{1 + \beta}{1 - \beta}}}}$

Here, the proposed last five equations in Table 1 are derived from the first three equations ([15] Panda and Yu, 2005; [9] Kaya and Atherton, 2001).

From the analytic equations for q_(a) and q_(b) in Table 1, we can obtain

$\begin{matrix} {{\tau = {p/\left( {4\chi_{2}} \right)}}{\chi_{2} = {\frac{1}{c_{2}}{\tanh \left( \chi_{2} \right)}}}{c_{2} = {1 - \frac{q_{a}}{2k^{2}h^{2}}}}} & \left\lbrack {{Equation}\mspace{20mu} 35} \right\rbrack \\ {{\tau = {{{p/\left( {4\chi_{3}} \right)} - {c_{3}\chi_{3}^{3}} + \chi_{3}} = {\tanh \left( \chi_{3} \right)}}}{c_{3} = {\frac{1}{3} - \frac{8q_{b}}{p^{2}k^{2}h^{2}}}}} & \left\lbrack {{Equation}\mspace{20mu} 36} \right\rbrack \end{matrix}$

Here, the equations 34, 35 and 36 guarantee an exact modeling result for first order plus time delay processes. However, these methods need solving one-dimensional nonlinear equations. So, for a simpler application, the following approximations are obtained.

$\begin{matrix} {\chi_{1} = \left\lbrack {2 + \frac{1 - {6\left( {1 - {c_{1}/2}} \right)^{4}}}{1 - {2\left( {c_{1}/2} \right)^{{- 1}/{({c_{1} - 1})}}}}} \right\rbrack^{1/{({c_{1} - 1})}}} & \left\lbrack {{Equation}\mspace{20mu} 37} \right\rbrack \\ {\chi_{2} = {\frac{1}{c_{2}}\tanh \; \left( \sqrt{15\frac{1 - c_{2}}{{6c_{2}} - 1}} \right)}} & \left\lbrack {{Equation}\mspace{20mu} 38} \right\rbrack \\ {\chi_{3} = \frac{\sqrt{1 - \sqrt{c_{3}\tanh \; \left( {1/\sqrt{c_{3}}} \right.}}}{\sqrt{c_{3}}}} & \left\lbrack {{Equation}\mspace{20mu} 39} \right\rbrack \end{matrix}$

These equations are obtaining by applying the perturbation analysis to the equations of the table 1.

From equation 19 and |G(jω)|=k/√{square root over (τ²ω²+1)}, we can also obtain

$\begin{matrix} {\tau = {\frac{p}{2\pi}\sqrt{\frac{4p^{2}k^{2}h^{2}}{\pi^{4}q_{b}} - 1}}} & \left\lbrack {{Equation}\mspace{20mu} 40} \right\rbrack \end{matrix}$

In summary, we estimate the time constant of the FOPTD model using one of the proposed methods of equations 31, 33 and 40.

FIG. 8 is a view illustrating a modeling error when the methods of the equations 34˜36 and the approximate equations 37˜40 are used. The approximate equations show a very accurate result with less than 1% except for the equation 40. FIG. 9 is a view illustrating a modeling error when the measurement values a, b, q_(a) and q_(b) have measurement errors. The equation 32, which is a conventional method for obtaining a time constant from a, produces a result which is very sensitive to the measurement error. The above bad results are obtained since the variation of the time constant does not largely affect the value a of y_(a)(t) in the case that the process has a large time delay.

According to ([12] Luyben (2001)), the curvature factor is useful when the value of d/τ is approximately derived. In the present invention, the curvature factor based on the following equations is preferably recommended.

F=q _(a) /a ²   [Equation 41]

FIG. 10 is a view illustrating a curvature factor of [12] Luyben (2001) with respect to a first order plus time delay process and an equation 41. The equation 41 according to the present invention may be applied for determining which modeling method will be used before the modeling work is performed. For example, as shown in FIG. 8, the equation 32 may not be used when the value of the equation 41 is greater than about 0.7 (d/τ is about 1.0).

FIG. 11 is a view illustrating a performance comparison between the methods of the present invention and the conventional methods with respect to various processes. IAE (The Integral of Absolute Error) is defined as follows. The smaller the value of the IAE, the better the performance.

$\begin{matrix} {{IAE} = {\int_{0}^{2{\pi/p}}{\frac{{{G_{m}\left( {j\; \overset{\sim}{\omega}} \right)} - {G\left( {j\; \overset{\sim}{\omega}} \right)}}}{{G\left( {j\; \overset{\sim}{\omega}} \right)}}{\overset{\sim}{\omega}}}}} & \left\lbrack {{Equation}\mspace{20mu} 42} \right\rbrack \end{matrix}$

The methods (equations 34˜39) according to the present invention show excellent performances as compared to the conventional methods (equation 32).

6. Proposed Method for Critically Damped Second Order Plus Time Delay Model Estimation

The critically damped second order plus time delay model is as follows.

$\begin{matrix} {{G_{m}(s)} = \frac{k\; \exp \; \left( {- {ds}} \right)}{\left( {{\tau s} + 1} \right)^{2}}} & \left\lbrack {{Equation}\mspace{20mu} 43} \right\rbrack \end{matrix}$

For the steady state gain k, Equation 31 is used. From equation 24 and A_(ω=|G) _(m)(jω)|=k/((τω)²+1), we have

$\begin{matrix} {\tau = {\frac{p}{2\pi}\sqrt{\frac{2{khp}}{\pi^{2}\sqrt{q_{b}}} - 1}}} & \left\lbrack {{Equation}\mspace{20mu} 44} \right\rbrack \end{matrix}$

An analytic equation for the oscillation period ([15] Panda and Yu, 2005; [27] Yu, 2006) is

$\begin{matrix} {{{{\exp \left( {- \frac{d}{\tau}} \right)} + {c_{3}\frac{d}{\tau}} - c_{4}} = 0}{where}{c_{3} = \frac{2\beta}{1 + \beta}}{c_{4} = \frac{2\left( {{\left( {1 + {p/\left( {2\tau} \right)}} \right)\beta} + \beta^{2}} \right)}{\left( {1 + \beta} \right)^{2}}}{\beta = {\exp \left( {{- p}/\left( {2\tau} \right)} \right)}}} & \left\lbrack {{Equation}\mspace{20mu} 45} \right\rbrack \end{matrix}$

Equation 45 can be solved by the Newton-Raphson method with an initial estimate

$\begin{matrix} {\frac{d}{\tau} = {\frac{p}{2\tau}\left( {1 - {\frac{2}{\pi}{\arctan\left( \frac{2{\pi\tau}}{p} \right)}}} \right)}} & \left\lbrack {{Equation}\mspace{20mu} 46} \right\rbrack \end{matrix}$

7. Method for Second Order Plus Time Delay Model Estimation

A general second order plus time delay (SOPTD) model is

$\begin{matrix} {{G_{m}(s)} = \frac{k\; {\exp \left( {- {ds}} \right)}}{{a_{2}s^{2}} + {a_{1}s} + 1}} & \left\lbrack {{Equation}\mspace{20mu} 47} \right\rbrack \end{matrix}$

We use the process steady state gain information of equation 28, its derivative of equation 29, q_(b) of equation 17 and the oscillation period p. We solve

$\begin{matrix} {k = {{G_{m}(0)} = \frac{y_{bm}}{u_{bm}}}} & \left\lbrack {{Equation}\mspace{20mu} 48} \right\rbrack \\ {{k\left( {{- d} - a_{1}} \right)} = {G_{m}(0)}} & \left\lbrack {{Equation}\mspace{20mu} 49} \right\rbrack \\ {q_{b} = {{\frac{16h^{2}}{\pi^{2}\omega^{2}}\left( {{G_{m}({j\omega})}}^{2} \right)} = {\frac{16h^{2}}{\pi^{2}\omega^{2}}\frac{k^{2}}{\left( {1 - {a_{2}\omega^{2}}} \right)^{2} + \left( {a_{1}\omega} \right)^{2}}}}} & \left\lbrack {{Equation}\mspace{20mu} 50} \right\rbrack \\ {d = {\frac{p}{2}\left( {1 - {\arctan \left( {q_{c}/\omega} \right)} - {\arctan \; 2\left( {{1 - {a_{1}\omega^{2}}},{a_{2}\omega}} \right)}} \right)}} & \left\lbrack {{Equation}\mspace{20mu} 51} \right\rbrack \end{matrix}$

The computational procedure is as follows, which is shown in FIG. 4.

-   -   Step 0: From relay feedback response, obtain G_(m)(0),         G′_(m)(0), q_(b) and the oscillation period p. Let d=p/4.     -   Step 1: Calculate a₁ from equation 49.     -   Step 2: Calculate a₂ from equation 50.     -   Step 3: Adjust d and repeat Steps 1 and 2 so that equation 51 is         satisfied.

FIG. 12 is a view illustrating the IAE values of the methods invented with respect to the various processes. In most cases, the critically damped second order plus time delay model shows better results as compared to the first order plus time delay model of the equations 31, 33, 36 and 39. The second order plus time delay model has the most excellent results. However, the second order plus time delay model needs more measurements and iterative computations.

8. Simulation (Tuning Parameter Computation and Application)

Simulations are performed to investigate the performances of methods under sampling, discretization errors and noisy environments. The following process is considered.

$\begin{matrix} {{G(s)} = \frac{\exp \left( {{- 1.2}s} \right)}{\left( {s + 1} \right)^{2}\left( {{0.5s} + 1} \right)}} & \left\lbrack {{Equation}\mspace{20mu} 52} \right\rbrack \end{matrix}$

Uniformly distributed noise n(t) with mean value and magnitude of 0 and 0.1 is introduced to the output. For noisy responses, q_(a) should be adjusted ([3] Astrom and Hagglund, 1995) as

$\begin{matrix} {{q_{a} = {{\frac{2}{p}{\int_{t_{1}}^{t_{1} + p}{{y_{a}(t)}^{2}{t}}}} - {2{\overset{\_}{\sigma}}_{a}^{2}}}},{{\overset{\_}{\sigma}}_{a}^{2} = {\frac{1}{T}{\int_{0}^{T}{{n(t)}^{2}{t}}}}}} & \left\lbrack {{Equation}\mspace{20mu} 53} \right\rbrack \end{matrix}$

The noise characteristics, σ _(a) ²=∫₀ ^(T)n(t)²dt/T , can be estimated before starting the relay feedback test. Similarly, q_(b) needs to be adjusted as in q_(a) when noise is involved.

Simulation results are shown in FIG. 13. Models obtained are in Table 2 and their Nyquist plots are in FIG. 14. Table 2 is a modeling result of the methods proposed in the present invention.

TABLE 2 Output Run Noise G(0) G′(0) Identified Process No. Size (1.00)* (−3.70)* Models IAE $\frac{\exp \left( {{- 1.2}s} \right)}{\left( {s + 1} \right)^{2}\left( {{0.5s} + 1} \right)}$ 1 0 1.009 −3.780 $\frac{1.009\mspace{11mu} {\exp \left( {{- 2.210}\; s} \right)}}{{1.968s} + 1}$ 0.093 $\frac{1.009\mspace{11mu} {\exp \left( {{- 1.574}\; s} \right)}}{\left( {{1.107s} + 1} \right)^{2}}$ 0.019 $\frac{1.009\mspace{11mu} {\exp \left( {{- 1.565}\; s} \right)}}{{1.191s^{2}} + {2.215s} + 1}$ 0.013 2 0.1 1.025 −4.114 $\frac{1.025\mspace{11mu} {\exp \left( {{- 2.0961}\; s} \right)}}{{2.011s} + 1}$ 0.062 $\frac{1.025\mspace{11mu} {\exp \left( {{- 1.465}\; s} \right)}}{\left( {{1.112s} + 1} \right)^{2}}$ 0.039 $\frac{1.025\mspace{11mu} {\exp \left( {{- 1.657}\; s} \right)}}{{0.974s^{2}} + {2.457s} + 1}$ 0.085 3 0.1 1.002 −3.432 $\frac{1.002\mspace{11mu} {\exp \left( {{- 2.076}\; s} \right)}}{{2.030s} + 1}$ 0.076 $\frac{1.002\mspace{11mu} {\exp \left( {{- 1.443}\; s} \right)}}{\left( {{1.117s} + 1} \right)^{2}}$ 0.040 $\frac{1.002\mspace{11mu} {\exp \left( {{- 1.317}\; s} \right)}}{{1.724s^{2}} + {2.115s} + 1}$ 0.053 *Exact value

For the FOPTD model, estimation equations of 31, 33, 36 and 39 are used. Because the Equation 52 is an overdamped process with a moderate time delay, all models estimated have excellent agreement with the true model, as shown by the Nyquist plot of FIG. 14.

9. Conclusion

Without modifying the original relay feedback system, simple and accurate estimates of process ultimate data are easily obtained. We use various integrals of the relay responses instead of point data to reduce the effects of the high harmonic terms. For FOPTD processes, errors over 15% of the previous approaches in estimating the ultimate gain can be reduced to below 5%. We derived the equations to extract a FOPTD, CDPTD and SOPTD models from the process information at the steady state and near the ultimate frequency. They are very simple and can be applied easily to commercial PID controllers.

10. Appendix A (Orthogonality of Trigonometric Functions ([11] Kreyzig, 1999))

For a set of trigonometric functions, {1, cos(ωt), cos(2ωt), . . . , sin(ωt), sin(2ωt), . . .}, we have

$\begin{matrix} {{\frac{2}{p}{\int_{t}^{t + p}{{\sin \left( {n\; \omega \; t} \right)}{\sin \left( {m\; \omega \; t} \right)}{t}}}} = \left\{ {{\begin{matrix} {0,} & {n \neq m} \\ {1,} & {n = m} \end{matrix}\frac{2}{p}{\int_{t}^{t + p}{{\sin \left( {n\; \omega \; t} \right)}{\cos \left( {m\; \omega \; t} \right)}{t}}}} = {{0\frac{2}{p}{\int_{t}^{t + p}{{\cos \left( {n\; \omega \; t} \right)}{\cos \left( {m\; \omega \; t} \right)}{t}}}} = \left\{ {{\begin{matrix} {0,} & {n \neq m} \\ {1,} & {n = {m \neq 0}} \\ {2,} & {n = {m = 0}} \end{matrix}{for}\mspace{14mu} p} = {2{\pi/{\omega.}}}} \right.}} \right.} & \left\lbrack {{Equation}\mspace{20mu} 54} \right\rbrack \end{matrix}$

Applying these relationships, we can derive equations 16 and 17. For example, for a function of

ƒ(t)=α₀ +α₁ cos(ωt)+β₁ sin(ωt)+α₂ cos(2ωt)+β₂ sin(2ωt)+ . . . ,

$\begin{matrix} {{\frac{2}{p}{\int_{t}^{t + p}{{f(t)}^{2}{t}}}} = {{2\alpha_{0}^{2}} + \alpha_{1}^{2} + \beta_{1}^{2} + \alpha_{2}^{2} + \beta_{2}^{2} + \ldots}} & \left\lbrack {{Equation}\mspace{20mu} 55} \right\rbrack \end{matrix}$

11. Appendix B (Moment Analyses ([3]Astrom and Hagglund, 1995))

Since

$\begin{matrix} {{{{{\overset{\sim}{U}}_{a}(s)} \equiv {{\left( {1 - {\exp \left( {- {ps}} \right)}} \right){\int_{0}^{t_{1}}{{u_{a}(t)}{\exp \left( {- {st}} \right)}{t}}}} + {\int_{t_{1}}^{t_{1} + p}{{u_{a}(t)}{\exp \left( {- {st}} \right)}{t}}}}}{{we}\mspace{14mu} {have}}}\mspace{11mu}} & \left\lbrack {{Equation}\mspace{20mu} 56} \right\rbrack \\ {{{{\overset{\sim}{U}}_{a}^{\prime}(s)} \equiv {{p\; ^{- {ps}}{\int_{0}^{t_{1}}{{u_{a}(t)}^{- {st}}{t}}}} + {\left( {1 - ^{- {ps}}} \right){\int_{0}^{t_{1}}{\left( {- t} \right){u_{a}(t)}^{- {st}}{t}}}} + {\int_{t_{1}}^{t_{1} + p}{\left( {- t} \right){u_{a}(t)}^{- {st}}{t}}}}}{{{\overset{\sim}{U}}_{a}^{''}(s)} \equiv {{{- p^{2}}^{- {ps}}{\int_{0}^{t_{1}}{{u_{a}(t)}^{- {st}}{t}}}} - {2p\; ^{- {ps}}{\int_{0}^{t_{1}}{{{tu}_{a}(t)}^{- {st}}{t}}}} + {\left( {1 - ^{- {ps}}} \right){\int_{0}^{t_{1}}{t^{2}{u_{a}(t)}^{- {st}}{t}}}} + {\int_{1}^{t_{1} + p}{t^{2}{u_{a}(t)}^{- {st}}{t}}}}}} & \left\lbrack {{Equation}\mspace{20mu} 57} \right\rbrack \end{matrix}$

Then applying integration by parts:

$\begin{matrix} {\begin{matrix} {{{\overset{\sim}{U}}_{a}^{\prime}(0)} = {{p{\int_{0}^{t_{1}}{{u_{a}(t)}{t}}}} - {\int_{1}^{t_{1} + p}{{{tu}_{a}(t)}{t}}}}} \\ {= {{{pu}_{b}\left( t_{1} \right)} - {{{tu}_{b}(t)}{\int_{t_{1}}^{t_{1} + p}{+ {\int_{1}^{t_{1} + p}{{u_{b}(t)}{t}}}}}}}} \\ {= {\int_{t_{1}}^{t_{1} + p}{{u_{b}(t)}{t}}}} \end{matrix}\begin{matrix} {{{\overset{\sim}{U}}_{a}^{''}(0)} = {{{- p^{2}}{\int_{0}^{t_{1}}{{u_{a}(t)}{t}}}} - {2p{\int_{0}^{t_{1}}{{tu}_{a}{t}}}} + {\int_{1}^{t_{1} + p}{t^{2}{u_{a}(t)}{t}}}}} \\ {= {{2p{\int_{0}^{t_{1}}{{u_{b}(t)}{t}}}} - {2{\int_{1}^{t_{1} + p}{{{tu}_{b}(t)}{t}}}}}} \end{matrix}} & \left\lbrack {{Equation}\mspace{20mu} 58} \right\rbrack \end{matrix}$

where u_(b)(t)=∫₀ ¹u_(a)(t)dt and u_(b)(t) and u_(b)(t) are periodic after t=t₁.

By applying the same procedure to

{tilde over (Y)} _(a)(s)≡(1−exp(−ps))∫₀ ¹y_(a)(t)exp(−st)dt+∫ ₁ ^(1+p) y _(a)(t)exp(−st)dt   [Equation 59]

we can obtain equations for {tilde over (Y)}′_(a)(0) and {tilde over (Y)}″_(a)(0):

{tilde over (Y)}′ _(a)(0)=∫₁ ^(1+p) y _(b)(t)dt

{tilde over (Y)}″ _(a)(0)=2p∫ ₀ ¹ y _(b)(t)dt−2₁ ^(1+p) ty _(b)(t)dt   [Equation 60]

In the present invention, an autotuning method using an integral of a relay feedback response according to the present invention can greatly remove effects of harmonics by integrating a relay feedback signal and using the same, so that an ultimate data and frequency model of a process can be accurately obtained.

As the present invention may be embodied in several forms without departing from the spirit or essential characteristics thereof, it should also be understood that the above-described examples are not limited by any of the details of the foregoing description, unless otherwise specified, but rather should be construed broadly within its spirit and scope as defined in the appended claims, and therefore all changes and modifications that fall within the meets and bounds of the claims, or equivalences of such meets and bounds are therefore intended to be embraced by the appended claims. 

1. An autotuning method using an integral of a relay feedback response, the method comprising the steps of: integrating a process data; and removing any effects of harmonics after integrating the process data and computing an ultimate gain.
 2. The method of claim 1 wherein the ultimate gain is computed in a state that a phase angle of a process is −180°wherein a harmonic term is neglected.
 3. The method of claim 1 wherein the ultimate gain is obtained by $K_{cu} = \frac{2{hp}}{\pi^{2}b}$ wherein b is an oscillation magnitude of a y_(b)({tilde over (t)})−y_(bm) signal.
 4. The method of claim 1 wherein the ultimate gain is computed by: $K_{cu} = {\frac{16h}{\pi \left( {a + {6\pi \; {b/p}}} \right)} = \frac{1}{{\frac{1}{4}\left( \frac{\pi \; a}{4h} \right)} + {\frac{3}{4}\left( \frac{\pi^{2}b}{2{hp}} \right)}}}$
 5. The method of claim 1 wherein the ultimate gain is computed by $K_{cu} = \frac{4h}{\pi \sqrt{q_{a}}}$
 6. The method of claim 1 wherein the ultimate gain is computed by: $K_{cu} = \frac{2{hp}}{\pi^{2}\sqrt{q_{b}}}$
 7. The method of claim 6 further comprising the step of: providing a frequency process model and a parameter model after removing any effects of harmonics.
 8. The method of claim 7 wherein the frequency process model is obtained by obtaining an amplitude ratio based on: ${\overset{\Cap}{A}}_{\omega} = \frac{\pi \sqrt{q_{a}}}{4h}$
 9. The method of claim 7 wherein the frequency process model is obtained by obtaining a phase angle based on: $\varphi_{\omega} = {\arctan \; \left( \frac{p}{2\pi} \right)q_{c}}$
 10. The method of claim 7 wherein the frequency process model is obtained by obtaining an amplitude ratio based on: ${\overset{\Cap}{A}}_{\omega} = \frac{\pi^{2}\sqrt{q_{b}}}{2h\; p}$
 11. The method of claim 7 wherein a first order plus time delay model is obtained by obtaining a time constant based on: $\tau = {{\left( {\frac{b}{kh} + \frac{p}{4}} \right)/\ln}\; \left( \chi_{1} \right)}$ χ₁^(c₁) − 2χ₁ + 1 = 0 $c_{1} = {p/\left( {\frac{2b}{kh} + \frac{p}{2}} \right)}$
 12. The method of claim 7 wherein a first order plus time delay model is obtained by obtaining a time constant based on: τ = p/(4χ₂) $\chi_{2} = {\frac{1}{c_{2}}\tanh \; \left( \chi_{2} \right)}$ $c_{2} = {1 - \frac{q_{a}}{2k^{2}h^{2}}}$
 13. The method of claim 7 wherein a first order plus time delay model is obtained by obtaining a time constant based on: τ = p/(4χ₃) − c₃χ₃³ + χ₃ = tanh  (χ₃) $c_{3} = {\frac{1}{3} - \frac{8q_{b}}{p^{2}k^{2}h^{2}}}$
 14. The method of claim 7 wherein a first order plus time delay model is obtained by using an approximate equation of: $\chi_{1} = \left\lbrack {2 + \frac{1 - {6\left( {1 - {c_{1}/2}} \right)^{4}}}{1 - {2\left( {c_{1}/2} \right)^{{- 1}/{({c_{1} - 1})}}}}} \right\rbrack^{1/{({c_{1} - 1})}}$
 15. The method of claim 7 wherein a first order plus time delay model is obtained by using an approximate equation of: $\chi_{2} = {\frac{1}{c_{2}}{\tanh\left( \sqrt{15\frac{1 - c_{2}}{{6c_{2}} - 1}} \right)}}$
 16. The method of claim 7 wherein a first order plus time delay model is obtained by using an approximate equation of: $\chi_{3} = \frac{\sqrt{1 - \sqrt{c_{3}\tanh \; \left( {1/\sqrt{c_{3}}} \right.}}}{\sqrt{c_{3}}}$
 17. The method of claim 7 wherein a first order plus time delay model is obtained by obtaining a time constant based on: $\tau = {\frac{p}{2\pi}\sqrt{\frac{4p^{2}k^{2}h^{2}}{\pi^{2}q_{b}} - 1}}$
 18. The method of claim 7 further comprising the step of: estimating a ratio of a time delay and a time constant of a first order plus time delay process based on a curvature factor of: F=q _(a) /a ²
 19. The method of claim 7 further comprising the step of: obtaining a critically damped second order plus time delay model by obtaining a time constant based on: $\tau = {\frac{p}{2\pi}\sqrt{\frac{2{khp}}{\pi^{4}\sqrt{q_{b}}} - 1}}$
 20. The method of claim 7 further comprising the steps of: setting a second time delay model of ${{G_{m}(S)} = \frac{k\; \exp \; \left( {- {ds}} \right)}{{a_{2}s^{2}} + {a_{1}s} + 1}};$ obtaining G_(m)(0), G_(m)′(0), q_(b) and an oscillation period P from relay feedback response and determining d=p/4; computing a₁ from k(−d−a₁)=G¹ _(m)(0) after obtaining G_(m)(0), G′_(m)(0), q_(b) and the oscillation period P from relay feedback response and determining d=p/4; computing a₂ from $q_{b} = {{\frac{16h^{2}}{\pi^{2}\omega^{2}}\left( {{G_{m}\left( {j\; \omega} \right)}}^{2} \right)} = {\frac{16h^{2}}{\pi^{2}\omega^{2}}\frac{k^{2}}{\left( {1 - {a_{2}\omega^{2}}} \right)^{2} + \left( {a_{1}\omega} \right)^{2}}}}$ after computing a₁; and adjusting d so that $d = {\frac{p}{2}\left( {1 - {\arctan \; \left( {q_{c}/\omega} \right)} - {\arctan \; 2\left( {1 - {a_{1}{\omega^{2}.a_{2}}\omega}} \right)}} \right)}$ is satisfied after computing a₂.
 21. The method of claim 7 further comprising the step of: performing a tuning parameter computation and application in consideration with a process of ${G(s)} = \frac{\exp \; \left( {{- 1.2}s} \right)}{\left( {s + 1} \right)^{2}\left( {{0.5s} + 1} \right)}$ 